Machine Learning Approaches for Predicting Protein-Protein Interaction Networks
DOI:
https://doi.org/10.5281/zenodo.22687599Keywords:
protein-protein interaction, machine learning; deep learning, graph neural networks, protein language models, interactome predictionAbstract
Computational prediction of protein-protein interactions (PPIs) has moved, over roughly a decade, from shallow
classifiers trained on hand-crafted sequence and domain features to deep architectures that learn representations
directly from sequence, structure or network context, and most recently to structure-aware models built on top of
protein language models and AlphaFold-derived structural predictions. This review synthesises the major
methodological families used for PPI prediction -- sequence-based classifiers, network-topology-based link
prediction, structure-based docking and structure-aware deep learning, and protein-language-model-based
approaches such as D-SCRIPT and AlphaFold-Multimer-derived pipelines -- and compares their reported
performance on shared or comparable benchmarks, principally the Human Reference Interactome (HuRI),
STRING and BioGRID-derived datasets. Reported area-under-the-receiver-operating-characteristic (AUROC)
values across methods evaluated on held-out interactions from the same organism typically fall in the 0.85-0.95
range for the strongest sequence- and structure-aware deep learning approaches, but performance drops
substantially, often below 0.70 AUROC, when models are evaluated on proteins or organisms absent from training
data, a generalisation gap that we argue is the central unresolved problem in the field rather than a secondary
limitation. We further discuss how negative-sampling strategy, which varies substantially across benchmark
datasets and is rarely standardised, materially affects reported performance and complicates cross-study
comparison, and we review emerging structure-aware and multimodal approaches that combine sequence
embeddings with AlphaFold-predicted structural features as a partial response to the generalisation problem. We
conclude with a set of methodological recommendations -- standardised negative sampling, mandatory
cross-species or cross-protein-family held-out evaluation, and transparent reporting of training-test overlap -- that
we believe are necessary for the field to make its reported performance gains more comparable and more
trustworthy going forward.
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